Interwoven, cross-sector, situational and enduring solidarities: crisis, resistance and de-privatisation in care work
Bibliographic record
Abstract
During and prior to the COVID-19 pandemic, a predominantly female, significantly racialised, activist health and social care union in British Columbia, Canada, built and won decisive resistance/solidarity strategies not only for union members, but also for non-union and previously unionised workers, residents/patients, and the larger careseeking public. The formal and informal strategies were broad, inclusive, values-driven actions that raised wages and conditions while simultaneously extending and improving care. The article draws on and extends concepts used in moral economy and labour process theorising to argue that the three interlaced, gendered resistance strategies reflected interwoven, cross-sector, situational and enduring solidarities. The analysis in this article highlights the almost indivisible aspects of formal and informal, long contention and situational resistance strategies, and suggests that a moral economy of ‘care’ and ‘restoring fairness’ formed the core of the entwined solidarity narratives and resistance strategies successfully coming together during the time of COVID-19. The article contributes to further theorising of gendered resistance (informal, liminal, unpaid work), the moral economy and care work as part of the labour process (interwoven solidarity), and adds to theorising long resistance strategies (enduring solidarity), and situational resistance strategies (cross-sector solidarity) in care work contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.055 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".